"Traditional" sensitivity is defined for binary classification only.
Maybe micro-average is what you're looking for, but in the multiclass case without anything more specified, you'll merely be calculating accuracy. Perhaps quantiles of the scores returned by permutation_test_score will give you the CIs you seek. On 24 April 2017 at 01:50, Suranga Kasthurirathne <suranga...@gmail.com> wrote: > > Hello all, > > I'm looking at the confidence matrix and performance measures (precision, > recall, f-measure etc.) produced by scikit. > > It seems that scikit calculates these measures per each outcome class, and > then combines them into some sort of average. > > I would really like to see these measures presented in the traditional(?) > context, where sensitivity is TP / TP + FN. (and is combined, and NOT per > class!) > > If I were to take scikit predictions, and calculate sensitivity using the > above, then my results wont match up to what scikit says :( > > How can I switch to seeing overall performance measures, and not per > class? and also, how may I obtain 95% confidence intervals foreach of these > measures? > > -- > Best Regards, > Suranga > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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